We’re excited to reveal our brand new PyTorch Landscape. The PyTorch Landscape helps researchers, developers, and organizations easily locate useful, curated, community-built tools that augment the PyTorch core framework.
What the Landscape Offers
The Landscape visually organizes projects into three categories—Modeling, Training, and Optimizations—making finding relevant frameworks, libraries, and projects easy. Users can quickly locate curated, valuable tools for a variety of use cases that complement the PyTorch framework. Each tool that is part of the Landscape has been reviewed and vetted by PyTorch project experts. The projects in the Landscape are considered to be mature and healthy and provide valuable capabilities that complement the PyTorch framework in their respective domains.
Explore the AI Landscape
The Explore page presents platforms, tools, and libraries, each with a logo, description, and links to GitHub and further details. This categorized, visual approach simplifies discovery and provides quick access to essential technologies.
Guide Page: A Closer Look
For deeper insights, the Guide page expands on each project, highlighting methodologies and trends shaping AI development, from adversarial robustness to self-supervised learning. There are also project statistics provided for each project, including metrics such as number of stars, contributors, commit history, languages used, license, and other valuable metrics that provide an in-depth understanding of the project and how it may be used.
Tracking AI’s Growth: The Stats Page
The Stats page provides insights into AI development trends, tracking repository activity, programming languages, and industry funding data.
- Repositories: 117 repositories, 20.5k contributors, and 797.2k stars across 815MB of source code.
- Development Trends: Weekly commit activity over the last year.
- Licensing Breakdown: Repositories are categorized by license type.
- Funding & Acquisitions: Insights into investment trends, including funding rounds and acquisitions.
Why Use the PyTorch Landscape?
Finding useful and high quality open source projects that complement the PyTorch core system can be overwhelming. The PyTorch Landscape offers a clear, accessible way to explore the ecosystem of community-built tools, whether you’re researching, building models, or making strategic decisions.
Stay ahead with the PyTorch Landscape — your guide to the PyTorch Ecosystem.
Want to Contribute a Project to the PyTorch Landscape?
Have you built a useful open source tool that you would like to share with the PyTorch community? Then help us grow the Ecosystem by contributing your tool! You can find the instructions to apply here. We welcome all contributions from the community!
